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The Elements of Statistical Learning II

General Outline:

The course is the second part of a two semester course on Statistical Learning. The first part (WS 2003/2004) concentrated on chapters 1-10 of the book The Elements of Statistical Learning, Springer 2001, this follow up course consists of two parts. The first part will continue with chapters 11-14 of the book. The second part will deal with methods of Statistical Learning applied to problems in Bioinformatics. There will be two hours of lecture per week and one hour of tutorial (V2/1).

This course covers a subject that is relevant for computer scientists  in general as well as for other scientists involved in data analysis and modeling. It is not limited to the field of computational biology.

Lecturer: Jörg Rahnenführer

Tutor: Jörg Rahnenführer

Course language: English

Time and location:

Course: Weekly, Wednesdays 11-13, Building 46, Room 024.
Tutorial: Once every two weeks (see below for dates), Fridays 16-18, Building 46, Room 021.
Office hours: On appointment.

Target Group and Prerequisites:

The lecture is targeted to advanced students in math, computer science and science students with mathematical background.
Prerequisites: Vordiplom in Mathematics or Computer Science or equivalent. Students should know linear algebra and have basic knowledge in statistics.


Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2001. Readers of the course are encouraged to acquire this book.

Contents: Tentative course and tutorial schedule

Lecture Date Topic
Lecture 1 Wed April 21 Repetition - Overview - Outlook
Lecture 2 Wed April 28 Neural Networks (HTF chapter 11)
Lecture 3 Wed May 5 Support Vector Machines (HTF chapter 12)
Lecture 4 Wed May 12 Prototype Methods and Nearest-Neighbors (HTF chapter 13)
Lecture 5 Wed May 19 Unsupervised Learning I (HTF chapter 14)
Lecture 6 Wed May 26 Unsupervised Learning II (HTF chapter 14)
Lecture 7 Wed June 2 Kernel Methods (HTF chapter 6)
Lecture 8 Wed June 9 Low-level Analysis of Gene Expression Data
Lecture 9 Wed June 23 Classification in Gene Expression Data
Lecture 10 Wed June 30 Combining Gene Expression Data and Biological Network Data
Lecture 11 Wed July 7 Classification of Protein Structures
Lecture 12 Wed July 14 Learning with Mixtures of Trees

Tutorial Date Topic HW Assigned    HW Due   
Tutorial 0 Fri April 23 Introduction to R - Repetition HW 1  
Tutorial 1 Fri May 7 Linear Regression + Model Assessment HW 2 HW 1
Tutorial 2 Fri May 14 Neural Networks HW 3 HW 2
Tutorial 3 Fri May 28 Support Vector Machines HW 4 HW 3
Tutorial 4 Fri June 11 Nearest-Neighbors + Unsupervised Learning HW 5 HW 4
Tutorial 5 Fri June 25 Kernel Methods HW 6 HW 5
Tutorial 6 Fri July 9 Classification with gene expression data HW 7 HW 6
Tutorial 7 Fri July 16 Synopsis   HW 7

Embedding into the Curricula Computer Science and Bioinformatics

Both parts of this course fulfil the requirements for the curricula of computer science and bioinformatics as optional course with 6 resp. 4 credit points (Spezialvorlesung, 6 bzw. 4 Leistungspunkte).

Requirements for the course certificate:

50% of the homework points and final exam (most likely oral).

Course Material:


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